Ground penetrating radar (GPR) is a popular modality for use in locating buried objects because of its non-destructive utilization and fast data collection. Unfortunately, manual processing of the accumulated data is typically required to locate an object, which can be time-consuming and requires skill. In applications in which there is an abundance of possible objects, such as detecting rebar reinforcement in foundation construction and locating utilities, it can be especially challenging to locate the objects using GPR.
Various approaches have been developed with the goal of automating object location using GPR. For example, neural networks and image processing-based pattern recognition methods have been reported as being useful for this purpose, but they are sensitive to noise and fail to perform adequately in the presence of incomplete or highly disturbed hyperbolic patterns. Needed are systems and methods that automate the processing of GPR data and provide accurate and reliable estimates as to the location and depth of buried objects.